Fault tolerant file models for parallel file systems: introducing distribution patterns for every file

Parallelism in file systems is obtained by using several independent server nodes supporting one or more secondary storage devices. This approach increases the performance and scalability of the system, but a fault in one single node can stop the whole system. To avoid this problem, data must be stored using some kind of redundant technique, so any data stored in a faulty element can be recovered. Fault tolerance can be provided in I/O systems by using replication or RAID based schemes. However, most of the current systems apply the same technique for all files in the system. This paper describes the fault tolerance support provided by Expand, a parallel file system based on standard servers. This support can be applied to other parallel file systems with many benefices: fault tolerance at file level, flexible definition of fault tolerance scheme to be used, possibility to change the fault tolerant support used for a file, etc.

A collective I/O implementation based on inspector–executor paradigm

In this paper, we present a novel multiple phase I/O collective technique for generic block-cyclic distributions. The I/O technique is divided into two stages: inspector and executor. During the inspector stage, the communication pattern is computed and the required datatypes are automatically generated. This information is used during the executor stage in performing the communication and file accesses. The two stages are decoupled, so that for repetitive file access patterns, the computations from the inspector stage can be performed once and reused several times by the executor. This strategy allows to amortize the inspector cost over several I/O operations. In this paper, we evaluate the performance of multiple phase I/O collective technique and we compare it with other state of the art approaches. Experimental results show that for small access granularities, our method outperforms in the large majority of cases other parallel I/O optimizations techniques.

Efficient parallel processing with spin-wave nanoarchitectures

In this paper, we study the algorithm design aspects of three newly developed spin-wave architectures. The architectures are capable of simultaneously transmitting multiple signals using different frequencies, and allow for concurrent read/write operations. Using such features, we show a number of parallel and fault-tolerant routing schemes and introduce a set of generic parallel processing techniques that can be used for design of fast algorithms on these spin-wave architectures. We also present a set of application examples to illustrate the operation of the proposed generic parallel techniques.

A message passing strategy for array redistributions in a torus network

The array redistribution problem occurs in many important applications in parallel computing. In this paper, we consider this problem in a torus network. Tori are preferred to other multidimensional networks (like hypercubes) due to their better scalability (IEE Trans. Parallel Distrib. Syst. 50(10), 1201–1218, [2001]). We present a message combining approach that splits any array redistribution problem in a series of broadcasts where all sources send messages of the same size, thus a balanced traffic load is achieved. Unlike existing array redistribution algorithms, the scheme introduced in this work eliminates the need for data reorganization in the memory of the source and target processors. Moreover, the processing of the scheduled broadcasts is pipelined, thus the total cost of redistribution is reduced.

Efficient parallel Text Retrieval techniques on Bulk Synchronous Parallel (BSP)/Coarse Grained Multicomputers (CGM)

In this paper, we present efficient, scalable, and portable parallel algorithms for the off-line clustering, the on-line retrieval and the update phases of the Text Retrieval (TR) problem based on the vector space model and using clustering to organize and handle a dynamic document collection. The algorithms are running on the Coarse-Grained Multicomputer (CGM) and/or the Bulk Synchronous Parallel (BSP) model which are two models that capture within a few parameters the characteristics of the parallel machine. To the best of our knowledge, our parallel retrieval algorithms are the first ones analyzed under these specific parallel models. For all the phases of the proposed algorithms, we analytically determine the relevant communication and computation cost thereby formally proving the efficiency of the proposed solutions. In addition, we prove that our technique for the on-line retrieval phase performs very well in comparison to other possible alternatives in the typical case of a multiuser information retrieval (IR) system where a number of user queries are concurrently submitted to an IR system. Finally, we discuss external memory issues and show how our techniques can be adapted to the case when processors have limited main memory but sufficient disk capacity for holding their local data.

An implementation of parallel file distribution in an agent hierarchy

PC grid is a cost-effective grid-computing platform that attracts users by allocating to their massively parallel applications as many desktop computers as requested. However, a challenge is how to distribute necessary files to remote computing nodes that may be unconnected to the same network file system, equipped with insufficient disk space to keep entire files, and even powered off asynchronously. Targeting PC grid, the AgentTeamwork grid-computing middleware deploys a hierarchy of mobile agents to remote desktops so as to launch, monitor, check-point, and resume a parallel and distributed computing job. To achieve high-speed file distribution, AgentTeamwork takes advantage of its agent hierarchy. The system partitions files into stripes at the tree root if they are random-access files, duplicates them at each tree level if they are shared among all remote nodes, fragments them into smaller messages if they are too large to relay to a lower tree level, aggregates such messages in a larger fragment if they are in transit to the same subtree, and returns output files to the user along multi-paths established within the tree. To achieve fault-tolerant file delivery, each agent periodically takes a snapshot of in-transit and on-memory file messages with its user job, and thus resumes them from the latest snapshot when they crash accidentally. This paper presents an implementation and its competitive performance of AgentTeamwork’s file-distribution algorithm including file partitioning, transfer, check-pointing, and consistency maintenance.

Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems

Recently, multi-objective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds of algorithms can obtain a set of solutions with different trade-offs. This contribution analyzes different application alternatives in order to attain the desired accuracy/interpr-etability balance by maintaining the improved accuracy that a tuning of membership functions could give but trying to obtain more compact models. In this way, we propose the use of multi-objective evolutionary algorithms as a tool to get almost one improved solution with respect to a classic single objective approach (a solution that could dominate the one obtained by such algorithm in terms of the system error and number of rules). To do that, this work presents and analyzes the application of six different multi-objective evolutionary algorithms to obtain simpler and still accurate linguistic fuzzy models by performing rule selection and a tuning of the membership functions. The results on two different scenarios show that the use of expert knowledge in the algorithm design process significantly improves the search ability of these algorithms and that they are able to improve both objectives together, obtaining more accurate and at the same time simpler models with respect to the single objective based approach.

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Matching high performance approximate inverse preconditioning to architectural platforms

In this paper we examine the performance of parallel approximate inverse preconditioning for solving finite element systems, using a variety of clusters containing the Message Passing Interface (MPI) communication library, the Globus toolkit and the Open MPI open-source software. The techniques outlined in this paper contain parameters that can be varied so as to tune the execution to the underlying platform. These parameters include the number of CPUs, the order of the linear system (n) and the “retention parameter” (δ l) of the approximate inverse used as a preconditioner. Numerical results are presented for solving finite element sparse linear systems on platforms with various CPU types and number, different compilers, different File System types, different MPI implementations and different memory sizes.

On Parallel Thinning Algorithms: Minimal Non-simple Sets,P-simple Points and Critical Kernels

Critical kernels constitute a general framework in the category of abstract complexes for the study of parallel homotopic thinning in any dimension. In this article, we present new results linking critical kernels to minimal non-simple sets (MNS) and P-simple points, which are notions conceived to study parallel thinning in discrete grids. We show that these two previously introduced notions can be retrieved, better understood and enriched in the framework of critical kernels. In particular, we propose new characterizations which hold in dimensions 2, 3 and 4, and which lead to efficient algorithms for detecting P-simple points and minimal non-simple sets.

Impact of platform heterogeneity on the design of parallel algorithms for morphological processing of high-dimensional image data

The main objective of this paper is to describe a realistic framework to understand parallel performance of high-dimensional image processing algorithms in the context of heterogeneous networks of workstations (NOWs). As a case study, this paper explores techniques for mapping hyperspectral image analysis techniques onto fully heterogeneous NOWs. Hyperspectral imaging is a new technique in remote sensing that has gained tremendous popularity in many research areas, including satellite imaging and aerial reconnaissance. The automation of techniques able to transform massive amounts of hyperspectral data into scientific understanding in valid response times is critical for space-based Earth science and planetary exploration. Using an evaluation strategy which is based on comparing the efficiency achieved by an heterogeneous algorithm on a fully heterogeneous NOW with that evidenced by its homogeneous version on a homogeneous NOW with the same aggregate performance as the heterogeneous one, we develop a detailed analysis of parallel algorithms that integrate the spatial and spectral information in the image data through mathematical morphology concepts. For comparative purposes, performance data for the tested algorithms on Thunderhead (a large-scale Beowulf cluster at NASA’s Goddard Space Flight Center) are also provided. Our detailed investigation of the parallel properties of the proposed morphological algorithms provides several intriguing findings that may help image analysts in selection of parallel techniques and strategies for specific applications.

The 2007 IEEE CEC simulated car racing competition

This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.

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Model-based mapping of reconfigurable image registration on FPGA platforms

Image registration is a computationally intensive application in the medical imaging domain that places stringent requirements on performance and memory management efficiency. This paper develops techniques for mapping rigid image registration applications onto configurable hardware under real-time performance constraints. Building on the framework of homogeneous parameterized dataflow, which provides an effective formal model of design and analysis of hardware and software for signal processing applications, we develop novel methods for representing and exploring the hardware design space when mapping image registration algorithms onto configurable hardware. Our techniques result in an efficient framework for trading off performance and configurable hardware resource usage based on the constraints of a given application. Based on trends that we have observed when applying these techniques, we also present a novel architecture that enables dynamically-reconfigurable image registration. This proposed architecture has the ability to tune its parallel processing structure adaptively based on relevant characteristics of the input images.

OpenMP based parallel normalized direct methods for sparse finite element linear systems

A new parallel normalized exact inverse algorithm is presented for solving sparse symmetric finite element linear systems on symmetric multiprocessor systems (SMP), based upon an antidiagonal motion approach (“wave”-like pattern) for overcoming the data dependencies. The proposed algorithm was implemented using OpenMP directives. Numerical results, such as speedups and efficiency, are presented illustrating the efficient performance on a symmetric multiprocessor computer system, where the proposed algorithmic solution method achieves good speedups.

Toward the parallelization of GSL

In this paper, we present our joint efforts to design and develop parallel implementations of the GNU Scientific Library for a wide variety of parallel platforms. The multilevel software architecture proposed provides several interfaces: a sequential interface that hides the parallel nature of the library to sequential users, a parallel interface for parallel programmers, and a web services based interface to provide remote access to the routines of the library. The physical level of the architecture includes platforms ranging from distributed and shared-memory multiprocessors to hybrid systems and heterogeneous clusters. Several well-known operations arising in discrete mathematics and sparse linear algebra are used to illustrate the challenges, benefits, and performance of different parallelization approaches.

A new model for time-series forecasting using radial basis functions and exogenous data

In this paper, we present a new model for time-series forecasting using radial basis functions (RBFs) as a unit of artificial neural networks (ANNs), which allows the inclusion of exogenous information (EI) without additional pre-processing. We begin by summarizing the most well-known EI techniques used ad hoc, i.e., principal component analysis (PCA) and independent component analysis (ICA). We analyze the advantages and disadvantages of these techniques in time-series forecasting using Spanish bank and company stocks. Then, we describe a new hybrid model for time-series forecasting which combines ANNs with genetic algorithms (GAs). We also describe the possibilities when implementing the model on parallel processing systems.

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Performance analysis of fault-tolerant routing algorithm in wormhole-switched interconnections

With nowadays popularity of large-scale parallel computers, Multiprocessors System-on-Chip (MP-SoCs), multicomputers, cluster computers and peer-to-peer communication networks, fault-tolerant routing becomes an important issue in developing these systems. Fault-tolerant routing algorithms in such systems aim at providing continuous operations in the presence of one or more failures by allowing the graceful degradation of system. The Software-Based fault-tolerant routing scheme has been suggested as an efficient routing algorithm to preserve both communication performance and fault-tolerant demands in parallel computer systems. To study network performance, a number of different analytical models for fault-free routing algorithms have been proposed in the past literature. However, there has not been reported any similar analytical model of fault-tolerant routing in the presence of faulty components. This paper presents a new analytical modeling approach for determining the effects of failures in wormhole-switched 2-D tori using the fault-tolerant Software-Based scheme. More specifically, we describe a general model to derive mathematical expressions to investigate the performance behavior of routing algorithms confronting convex (|-shaped, □-shaped) or concave (U-shaped, +-shaped, T-shaped, H-shaped) faulty regions. The model is validated through comprehensive simulation experiments for different types of failures.

Inverse multi-objective robust evolutionary design

In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances efficiently.

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Methods for quantitative usability requirements: a case study on the development of the user interface of a mobile phone

Quantitative usability requirements are a critical but challenging, and hence an often neglected aspect of a usability engineering process. A case study is described where quantitative usability requirements played a key role in the development of a new user interface of a mobile phone. Within the practical constraints of the project, existing methods for determining usability requirements and evaluating the extent to which these are met, could not be applied as such, therefore tailored methods had to be developed. These methods and their applications are discussed.

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Using trust assumptions with security requirements

Assumptions are frequently made during requirements analysis of a system about the trustworthiness of its various components (including human components). These trust assumptions, whether implicit or explicit, affect the scope of the analysis, derivation of security requirements, and in some cases how functionality is realized. This paper presents trust assumptions in the context of analysis of security requirements. A running example shows how trust assumptions can be used by a requirements engineer to help define and limit the scope of analysis and to document the decisions made during the process. The paper concludes with a case study examining the impact of trust assumptions on software that uses the secure electronic transaction specification.

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